Differential phospho/protein analysis (Comparison t-test vs DEP) - CRC Batch 1
Sophie Herbst
Analysis date: 2023-06-19
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")TODO
- Do differential abudance analysis for prep batch and mass spec run
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
Test_PhosphoData(pY_Set1_form, comparison = "E", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test## Testing non-log transformed changes with t-test and adjusting p-value using FDR. Significance level = 0.05data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Return_DEP_Hits_Plots(data = pY_Set1_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(comparison)
##
## # Now:
## data %>% select(all_of(comparison))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## 'select()' returned 1:1 mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.3219316
## 2: ABC transporter disorders 0.3501006
## 3: ABC-family proteins mediated transport 0.3501006
## 4: ADP signalling through P2Y purinoceptor 1 0.2273642
## 5: ALK mutants bind TKIs 0.6953125
## 6: APC/C-mediated degradation of cell cycle proteins 0.9683698
## padj log2err ES NES size leadingEdge
## 1: 0.8772981 0.10473282 0.8430233 1.1431478 1 6385
## 2: 0.8772981 0.09957912 0.8255814 1.1194965 1 5687
## 3: 0.8772981 0.09957912 0.8255814 1.1194965 1 5687
## 4: 0.8772981 0.12814292 0.8895349 1.2062180 1 1432
## 5: 0.9726046 0.06143641 -0.6569767 -0.8823673 1 1213
## 6: 0.9812774 0.05617666 0.3859649 0.6052109 2 5687
## Note: Row-scaling applied for this heatmap
Test_PhosphoData(pY_Set1_form, comparison = "EC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test## Testing non-log transformed changes with t-test and adjusting p-value using FDR. Significance level = 0.05data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.6124031
## 2: ABC transporter disorders 0.7926357
## 3: ABC-family proteins mediated transport 0.7926357
## 4: ADP signalling through P2Y purinoceptor 1 0.2984496
## 5: ALK mutants bind TKIs 0.6404040
## 6: APC/C-mediated degradation of cell cycle proteins 0.6906077
## padj log2err ES NES size leadingEdge
## 1: 0.97396 0.06720651 0.6918605 0.9340944 1 6385
## 2: 0.97396 0.05490737 0.5813953 0.7849533 1 5687
## 3: 0.97396 0.05490737 0.5813953 0.7849533 1 5687
## 4: 0.97396 0.10714024 0.8662791 1.1695804 1 1432
## 5: 0.97396 0.06705126 -0.6744186 -0.9142164 1 1213
## 6: 0.97396 0.05896945 -0.5518113 -0.8639961 2 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")Test_PhosphoData(pY_Set1_form, comparison = "EBC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test## Testing non-log transformed changes with t-test and adjusting p-value using FDR. Significance level = 0.05data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.4151329
## 2: ABC transporter disorders 0.9213052
## 3: ABC-family proteins mediated transport 0.9213052
## 4: ADP signalling through P2Y purinoceptor 1 0.8875256
## 5: ALK mutants bind TKIs 0.2053743
## 6: APC/C-mediated degradation of cell cycle proteins 0.4480409
## padj log2err ES NES size leadingEdge
## 1: 0.9374656 0.09054289 -0.7790698 -1.0625762 1 6385
## 2: 0.9646724 0.04754342 0.5348837 0.7313084 1 5687
## 3: 0.9646724 0.04754342 0.5348837 0.7313084 1 5687
## 4: 0.9646724 0.05216303 -0.5581395 -0.7612486 1 1432
## 5: 0.8992707 0.13214726 0.8779070 1.2002996 1 1213
## 6: 0.9374656 0.07647671 -0.6679829 -1.0309284 2 983
## Note: Row-scaling applied for this heatmap
EC vs E
Test_PhosphoData(pY_Set1_form, comparison = "EC", comparison_base = "E") %>% GGPlotly_Volcano_Test## Testing non-log transformed changes with t-test and adjusting p-value using FDR. Significance level = 0.05data_diff_EC_vs_E_pY <- test_diff(pY_se_Set1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2920696
## 2: ABC transporter disorders 0.1566731
## 3: ABC-family proteins mediated transport 0.1566731
## 4: ADP signalling through P2Y purinoceptor 1 0.8142857
## 5: ALK mutants bind TKIs 0.1959184
## 6: APC/C-mediated degradation of cell cycle proteins 0.3647059
## padj log2err ES NES size leadingEdge
## 1: 0.6828670 0.10839426 -0.8430233 -1.1437044 1 6385
## 2: 0.6329398 0.15419097 -0.9127907 -1.2383558 1 5687
## 3: 0.6329398 0.15419097 -0.9127907 -1.2383558 1 5687
## 4: 0.9108626 0.05605959 0.5930233 0.7873775 1 1432
## 5: 0.6486362 0.14040624 0.9069767 1.2042244 1 1213
## 6: 0.7323529 0.10672988 -0.7251462 -1.1327565 2 5687,983#data_results <- get_df_long(dep)EBC vs EC
Test_PhosphoData(pY_Set1_form, comparison = "EBC", comparison_base = "EC") %>% GGPlotly_Volcano_Test## Testing non-log transformed changes with t-test and adjusting p-value using FDR. Significance level = 0.05data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.1011673
## 2: ABC transporter disorders 0.9533074
## 3: ABC-family proteins mediated transport 0.9533074
## 4: ADP signalling through P2Y purinoceptor 1 0.1595331
## 5: ALK mutants bind TKIs 0.3385214
## 6: APC/C-mediated degradation of cell cycle proteins 0.8342541
## padj log2err ES NES size leadingEdge
## 1: 0.4419371 0.19578900 -0.9476744 -1.2667050 1 6385
## 2: 0.9748400 0.04660151 -0.5232558 -0.6994077 1 5687
## 3: 0.9748400 0.04660151 -0.5232558 -0.6994077 1 5687
## 4: 0.5884998 0.15315881 -0.9186047 -1.2278491 1 1432
## 5: 0.8265848 0.09957912 -0.8313953 -1.1112811 1 1213
## 6: 0.9748400 0.05019343 -0.5263158 -0.7832006 2 983,5687#data_results <- get_df_long(dep)Serine/Threonine fractionated
Each condition vs ctrl
#Test_PhosphoData(pST_Set1_form, comparison = "E", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.4884211 0.8732408
## 2: ABC transporter disorders 0.4390681 0.8732408
## 3: ABC-family proteins mediated transport 0.9052632 0.9743672
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.3179298 0.8732408
## 5: ADP signalling through P2Y purinoceptor 1 0.1797323 0.7981031
## 6: AKT phosphorylates targets in the cytosol 0.3315789 0.8732408
## log2err ES NES size leadingEdge
## 1: 0.08312913 -0.5815003 -1.0045958 3 11168,2547
## 2: 0.08020234 0.5617310 1.0556435 5 5684,5707,9491,5708
## 3: 0.04424074 0.3164369 0.6180787 6 5684,5707,4363,9491
## 4: 0.10027911 0.6518453 1.1434323 4 5576,5573
## 5: 0.14205664 -0.9150889 -1.2188049 1 5321
## 6: 0.09466462 0.5827565 1.1382658 6 7249,84335,207,572,2931
## Note: Row-scaling applied for this heatmap
#Test_PhosphoData(pST_Set1_form, comparison = "EC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.7790262 0.9423051
## 2: ABC transporter disorders 0.3752711 0.8362723
## 3: ABC-family proteins mediated transport 0.6033403 0.8723999
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.2838710 0.7967630
## 5: ADP signalling through P2Y purinoceptor 1 0.3073930 0.7967630
## 6: AKT phosphorylates targets in the cytosol 0.6450939 0.8848830
## log2err ES NES size leadingEdge
## 1: 0.05412006 -0.4836301 -0.8035502 3 11168,2547,3159
## 2: 0.09992770 0.5650003 1.0950764 5 5707,9491,5708,55788,5684
## 3: 0.07165274 0.4391696 0.8874789 6 5707,9491,4363
## 4: 0.11724972 0.6561695 1.2011347 4 5576,5573
## 5: 0.10552094 0.8531268 1.1330559 1 5321
## 6: 0.06831109 0.4199677 0.8486755 6 7249,84335,2931
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")#Test_PhosphoData(pST_Set1_form, comparison = "EBC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.1364366 0.7522345
## 2: ABC transporter disorders 0.1638889 0.7539830
## 3: ABC-family proteins mediated transport 0.8616541 0.9691266
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.6256831 0.8890290
## 5: ADP signalling through P2Y purinoceptor 1 0.2534930 0.7664718
## 6: AKT phosphorylates targets in the cytosol 0.8160237 0.9509037
## log2err ES NES size leadingEdge
## 1: 0.15016980 -0.7587593 -1.3124585 3 2547,3159,11168
## 2: 0.18302394 0.5767683 1.3037564 5 9491,5707,55788,5708,5684
## 3: 0.03943665 -0.3321983 -0.6955622 6 23
## 4: 0.08383611 0.4209305 0.8668766 4 5576,5573
## 5: 0.11988785 -0.8703385 -1.1636827 1 5321
## 6: 0.07417590 0.3029458 0.7300750 6 84335,207,1026EC vs E
#Test_PhosphoData(pST_Set1_form, comparison = "EC", comparison_base = "E") %>% GGPlotly_Volcano_Test
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.427792916
## 2: ABC transporter disorders 0.493525180
## 3: ABC-family proteins mediated transport 0.779944290
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.503681885
## 5: ADP signalling through P2Y purinoceptor 1 0.013641932
## 6: AKT phosphorylates targets in the cytosol 0.008241885
## padj log2err ES NES size leadingEdge
## 1: 0.9885312 0.10592029 0.5614847 1.0256258 3 11168,2547
## 2: 0.9885312 0.06321912 -0.5018511 -1.0017106 5 5684
## 3: 0.9885312 0.04049348 -0.3692819 -0.7751279 6 5684
## 4: 0.9885312 0.06335970 -0.5293804 -0.9870411 4 5576,5577,5573
## 5: 0.6825367 0.38073040 0.9913941 1.3389465 1 5321
## 6: 0.6479679 0.38073040 -0.7830913 -1.6437198 6 572,84335,7249,207
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
#Test_PhosphoData(pST_Set1_form, comparison = "EBC", comparison_base = "EC") %>% GGPlotly_Volcano_Test
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.14456036 0.7828541
## 2: ABC transporter disorders 0.60357143 0.8944963
## 3: ABC-family proteins mediated transport 0.36122178 0.8210576
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.69121813 0.9042184
## 5: ADP signalling through P2Y purinoceptor 1 0.05642023 0.6434396
## 6: AKT phosphorylates targets in the cytosol 0.30923695 0.8202010
## log2err ES NES size leadingEdge
## 1: 0.13959967 -0.7489948 -1.2676540 3 3159,11168,2547
## 2: 0.10135074 0.3812536 0.8866448 5 55788,5708,9491,5684
## 3: 0.07473852 -0.5384335 -1.0991481 6 23,4363
## 4: 0.04678830 -0.4540862 -0.8372966 4 5576,5577
## 5: 0.26635066 -0.9787722 -1.2999630 1 5321
## 6: 0.15851411 0.4545464 1.1251306 6 1026,572,207,84335
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Serine/Threonine one shot
Each condition vs ctrl
#Test_PhosphoData(pST_Set1_oneshot_form, comparison = "E", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_E_vs_ctrl_pST_oneshot <- test_diff(pST_se_Set1_oneshot, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pST_oneshot <- add_rejections_SH(data_diff_E_vs_ctrl_pST_oneshot, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST_oneshot, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_oneshot_form, dep_E_vs_ctrl_pST_oneshot, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.55458515 0.8677316
## 2: ABC transporter disorders 0.29690722 0.8188248
## 3: ABC-family proteins mediated transport 0.69126214 0.9109398
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.07572816 0.6744680
## 5: AKT phosphorylates targets in the cytosol 0.82785300 0.9580505
## 6: ALK mutants bind TKIs 0.17361111 0.7510412
## log2err ES NES size leadingEdge
## 1: 0.07829552 -0.5072386 -0.9032856 3 11168,3159
## 2: 0.11146267 0.8545455 1.1470004 1 5684
## 3: 0.06143641 0.5572177 0.8477599 2 5684,23
## 4: 0.22798720 0.9016990 1.3718593 2 5576,5573
## 5: 0.05280500 -0.5915152 -0.7872001 1 7249
## 6: 0.13725078 0.6578377 1.2853796 5 5573,27436,1213,6801
## Note: Row-scaling applied for this heatmap
#Test_PhosphoData(pST_Set1_oneshot_form, comparison = "EC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_EC_vs_ctrl_pST_oneshot <- test_diff(pST_se_Set1_oneshot, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST_oneshot <- add_rejections_SH(data_diff_EC_vs_ctrl_pST_oneshot, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST_oneshot, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_oneshot_form, dep_EC_vs_ctrl_pST_oneshot, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.69052632 0.9827737
## 2: ABC transporter disorders 0.69488189 0.9827737
## 3: ABC-family proteins mediated transport 0.42959002 0.9157638
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.13151927 0.7502400
## 5: AKT phosphorylates targets in the cytosol 0.95669291 0.9919874
## 6: ALK mutants bind TKIs 0.09368192 0.7099567
## log2err ES NES size leadingEdge
## 1: 0.06538342 0.4572435 0.8063100 3 3159,11168,7518
## 2: 0.06184060 -0.6545455 -0.8774269 1 5684
## 3: 0.08108021 -0.6553398 -1.0345104 2 23,5684
## 4: 0.18470647 0.8662491 1.3300696 2 5576,5573
## 5: 0.04697587 -0.5175758 -0.6938172 1 7249
## 6: 0.21654284 0.7422244 1.5241204 5 1213,27436,6801,5573,4869
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST_oneshot, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")#Test_PhosphoData(pST_Set1_oneshot_form, comparison = "EBC", comparison_base = "ctrl") %>% GGPlotly_Volcano_Test
data_diff_EBC_vs_ctrl_pST_oneshot <- test_diff(pST_se_Set1_oneshot, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST_oneshot <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST_oneshot, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST_oneshot, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_oneshot_form, dep_EBC_vs_ctrl_pST_oneshot, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.826606876
## 2: ABC transporter disorders 0.773195876
## 3: ABC-family proteins mediated transport 0.533980583
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.006799441
## 5: AKT phosphorylates targets in the cytosol 0.481624758
## 6: ALK mutants bind TKIs 0.020092383
## padj log2err ES NES size leadingEdge
## 1: 0.9402403 0.04107133 -0.4176059 -0.7462942 3 3159,11168
## 2: 0.9194048 0.05896945 -0.6060606 -0.8056837 1 5684
## 3: 0.9194048 0.06508776 -0.6067961 -0.9614707 2 23,5684
## 4: 0.2871996 0.40701792 0.9538835 1.5451380 2 5576,5573
## 5: 0.9194048 0.07934350 0.7709091 1.0200925 1 7249
## 6: 0.3168414 0.35248786 0.7259440 1.6927374 5 5573,27436,6801,4869,1213## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
EC vs E
#Test_PhosphoData(pST_Set1_oneshot_form, comparison = "EC", comparison_base = "E") %>% GGPlotly_Volcano_Test
data_diff_EC_vs_E_pST_oneshot <- test_diff(pST_se_Set1_oneshot, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST_oneshot <- add_rejections_SH(data_diff_EC_vs_E_pST_oneshot, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST_oneshot, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set1_oneshot_form, dep_EC_vs_E_pST_oneshot, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.## pathway pval padj
## 1: 2-LTR circle formation 0.30573248 0.9954594
## 2: ABC transporter disorders 0.05182342 0.6249294
## 3: ABC-family proteins mediated transport 0.08794788 0.8195144
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.92182410 0.9954594
## 5: AKT phosphorylates targets in the cytosol 0.95010395 0.9954594
## 6: ALK mutants bind TKIs 0.95786517 0.9954594
## log2err ES NES size leadingEdge
## 1: 0.14040624 0.6081637 1.1278061 3 3159,11168
## 2: 0.27650060 -0.9757576 -1.2980569 1 5684
## 3: 0.19189224 -0.8714427 -1.3446052 2 5684
## 4: 0.04000315 -0.4524340 -0.6980896 2 5573
## 5: 0.04979032 0.5200000 0.6932750 1 7249
## 6: 0.03120530 -0.3004947 -0.5875252 5 5573,27436## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
#Test_PhosphoData(pST_Set1_oneshot_form, comparison = "EBC", comparison_base = "EC") %>% GGPlotly_Volcano_Test
data_diff_EBC_vs_EC_pST_oneshot <- test_diff(pST_se_Set1_oneshot, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST_oneshot <- add_rejections_SH(data_diff_EBC_vs_EC_pST_oneshot, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST_oneshot, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_oneshot_form, dep_EBC_vs_EC_pST_oneshot, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.1170370 0.5876732
## 2: ABC transporter disorders 0.8133874 0.9592315
## 3: ABC-family proteins mediated transport 0.7240829 0.9592315
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.6794258 0.9592315
## 5: AKT phosphorylates targets in the cytosol 0.2454361 0.7566075
## 6: ALK mutants bind TKIs 0.6816380 0.9592315
## log2err ES NES size leadingEdge
## 1: 0.15631240 -0.7530280 -1.3088694 3 3159
## 2: 0.05582647 0.5866667 0.7898633 1 5684
## 3: 0.05009229 -0.5267946 -0.8318367 2 23
## 4: 0.05302125 -0.5463292 -0.8626828 2 5576
## 5: 0.12325723 0.8824242 1.1880589 1 7249
## 6: 0.04424074 -0.4274849 -0.8502453 5 1213
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Motif enrichment analysis
7AA Sequences pST fractionated
all_pST_sites <- (rowData(dep_E_vs_ctrl_pST) %>% as_tibble()) %>% select( Annotated_Sequence, HGNC_Symbol, Master.Protein.Accessions) %>% unique
table(all_pST_sites$Master.Protein.Accessions %in% Phosphorylation_site_dataset$ACC_ID )##
## FALSE TRUE
## 15 3923Phosphorylation_site_dataset_pST <- Phosphorylation_site_dataset %>% filter(ACC_ID %in% all_pST_sites$Master.Protein.Accessions)
# TODO: s/t in first place might have TMT and or phos
# TODO did not account for double phosphorylated peptides
extracted_trimmed_peptide <- function(peptide){
str_extract(peptide, "s|t")
if( nchar( sub("[ts].*", "", peptide )) >= 7 &
nchar( sub(".*[ts]", "", peptide )) >= 7){
pep_trimmed <- str_extract(peptide, ".......[ts].......")
}else if(nchar( sub("[ts].*", "", peptide )) < 7 &
nchar( sub(".*[ts]", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".*[ts].*")
}else if(nchar( sub("[ts].*", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".*[ts].......")
}else if(nchar( sub(".*[ts]", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".......[ts].*")
}else{stop(paste("Could not extract trimmed sequenxce from", peptide )) }
return(pep_trimmed)
}
all_pST_sites$trimmed_sequence <- sapply(all_pST_sites$Annotated_Sequence, extracted_trimmed_peptide, simplify = T)
get_plusminus_7AA_sequence <- function(peptide, protein){
mod_peptide <- paste0(str_to_upper(sub("[ts].*", "", peptide) ),
str_extract(peptide, "[ts]"),
str_to_upper(sub(".*[ts]", "", peptide) ) )
sequ <- Phosphorylation_site_dataset_pST %>% filter(ACC_ID == protein) %>%
mutate( SEQUENCE = paste0(str_to_upper(str_sub(`SITE_+/-7_AA`, 1, 7)),
str_sub(`SITE_+/-7_AA`, 8,8),
paste0(str_to_upper(str_sub(`SITE_+/-7_AA`, 9, 15)))) ) %>%
filter(str_detect(SEQUENCE, mod_peptide)) %>% .$SEQUENCE
if(length(sequ) == 0){
return(NA)}else{return(sequ)}
}
sequences7AA <- sapply(1:nrow(all_pST_sites), function(i){
sequ <- get_plusminus_7AA_sequence(peptide = all_pST_sites$trimmed_sequence[i],
protein = all_pST_sites$Master.Protein.Accessions[i]) %>% unique
if(length(sequ) != 1){
return(NA)}else{return(sequ)}
}, simplify = T)
table(is.na(sequences7AA) )##
## FALSE TRUE
## 3493 445all_pST_sites$Sequence_7_AA <- sequences7AA7AA Sequences pST one shot
all_pST_oneshot_sites <- (rowData(dep_E_vs_ctrl_pST_oneshot) %>% as_tibble()) %>% select( Annotated_Sequence, HGNC_Symbol, Master.Protein.Accessions) %>% unique
table(all_pST_oneshot_sites$Master.Protein.Accessions %in% Phosphorylation_site_dataset$ACC_ID )##
## FALSE TRUE
## 1 1446Phosphorylation_site_dataset_pST_oneshot <- Phosphorylation_site_dataset %>% filter(ACC_ID %in% all_pST_oneshot_sites$Master.Protein.Accessions)
# TODO: s/t in first place might have TMT and or phos
# TODO did not account for double phosphorylated peptides
extracted_trimmed_peptide <- function(peptide){
str_extract(peptide, "s|t")
if( nchar( sub("[ts].*", "", peptide )) >= 7 &
nchar( sub(".*[ts]", "", peptide )) >= 7){
pep_trimmed <- str_extract(peptide, ".......[ts].......")
}else if(nchar( sub("[ts].*", "", peptide )) < 7 &
nchar( sub(".*[ts]", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".*[ts].*")
}else if(nchar( sub("[ts].*", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".*[ts].......")
}else if(nchar( sub(".*[ts]", "", peptide )) < 7){
pep_trimmed <- str_extract(peptide, ".......[ts].*")
}else{stop(paste("Could not extract trimmed sequenxce from", peptide )) }
return(pep_trimmed)
}
all_pST_oneshot_sites$trimmed_sequence <- sapply(all_pST_oneshot_sites$Annotated_Sequence, extracted_trimmed_peptide, simplify = T)
get_plusminus_7AA_sequence <- function(peptide, protein){
mod_peptide <- paste0(str_to_upper(sub("[ts].*", "", peptide) ),
str_extract(peptide, "[ts]"),
str_to_upper(sub(".*[ts]", "", peptide) ) )
sequ <- Phosphorylation_site_dataset_pST_oneshot %>% filter(ACC_ID == protein) %>%
mutate( SEQUENCE = paste0(str_to_upper(str_sub(`SITE_+/-7_AA`, 1, 7)),
str_sub(`SITE_+/-7_AA`, 8,8),
paste0(str_to_upper(str_sub(`SITE_+/-7_AA`, 9, 15)))) ) %>%
filter(str_detect(SEQUENCE, mod_peptide)) %>% .$SEQUENCE
if(length(sequ) == 0){
return(NA)}else{return(sequ)}
}
sequences7AA <- sapply(1:nrow(all_pST_oneshot_sites), function(i){
sequ <- get_plusminus_7AA_sequence(peptide = all_pST_oneshot_sites$trimmed_sequence[i],
protein = all_pST_oneshot_sites$Master.Protein.Accessions[i]) %>% unique
if(length(sequ) != 1){
return(NA)}else{return(sequ)}
}, simplify = T)
table(is.na(sequences7AA) )##
## FALSE TRUE
## 1295 152all_pST_oneshot_sites$Sequence_7_AA <- sequences7AAE vs ctrl
E_vs_ctrl_pST_7AA <- left_join( (rowData(dep_E_vs_ctrl_pST) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = E_vs_ctrl_diff,
p = E_vs_ctrl_p.adj)
%>% unique ),
(all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
E_vs_ctrl_pST_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/E_vs_ctrl_pST.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")E vs ctrl on shot
E_vs_ctrl_pST_oneshot_7AA <- left_join( (rowData(dep_E_vs_ctrl_pST_oneshot) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = E_vs_ctrl_diff,
p = E_vs_ctrl_p.adj)
%>% unique ),
(all_pST_oneshot_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
E_vs_ctrl_pST_oneshot_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/E_vs_ctrl_pST_oneshot.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EC vs ctrl
EC_vs_ctrl_pST_7AA <- left_join( (rowData(dep_EC_vs_ctrl_pST) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EC_vs_ctrl_diff,
p = EC_vs_ctrl_p.adj)
%>% unique ),
(all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EC_vs_ctrl_pST_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EC_vs_ctrl_pST.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EC vs ctrl on shot
EC_vs_ctrl_pST_oneshot_7AA <- left_join( (rowData(dep_EC_vs_ctrl_pST_oneshot) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EC_vs_ctrl_diff,
p = EC_vs_ctrl_p.adj)
%>% unique ),
(all_pST_oneshot_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EC_vs_ctrl_pST_oneshot_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EC_vs_ctrl_pST_oneshot.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EC vs E
EC_vs_E_pST_7AA <- left_join( (rowData(dep_EC_vs_E_pST) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EC_vs_E_diff,
p = EC_vs_E_p.adj)
%>% unique ),
(all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EC_vs_E_pST_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EC_vs_E_pST.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EC vs E on shot
EC_vs_E_pST_oneshot_7AA <- left_join( (rowData(dep_EC_vs_E_pST_oneshot) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EC_vs_E_diff,
p = EC_vs_E_p.adj)
%>% unique ),
(all_pST_oneshot_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EC_vs_E_pST_oneshot_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EC_vs_E_pST_oneshot.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EBC vs ctrl
EBC_vs_ctrl_pST_7AA <- left_join( (rowData(dep_EBC_vs_ctrl_pST) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EBC_vs_ctrl_diff,
p = EBC_vs_ctrl_p.adj)
%>% unique ),
(all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EBC_vs_ctrl_pST_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EBC_vs_ctrl_pST.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EBC vs ctrl on shot
EBC_vs_ctrl_pST_oneshot_7AA <- left_join( (rowData(dep_EBC_vs_ctrl_pST_oneshot) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EBC_vs_ctrl_diff,
p = EBC_vs_ctrl_p.adj)
%>% unique ),
(all_pST_oneshot_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EBC_vs_ctrl_pST_oneshot_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EBC_vs_ctrl_pST_oneshot.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EBC vs EC
EBC_vs_EC_pST_7AA <- left_join( (rowData(dep_EBC_vs_EC_pST) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EBC_vs_EC_diff,
p = EBC_vs_EC_p.adj)
%>% unique ),
(all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EBC_vs_EC_pST_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EBC_vs_EC_pST.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")EBC vs EC on shot
EBC_vs_EC_pST_oneshot_7AA <- left_join( (rowData(dep_EBC_vs_EC_pST_oneshot) %>% as_tibble() %>%
select(annotation = ID, Annotated_Sequence, HGNC_Symbol,
fc = EBC_vs_EC_diff,
p = EBC_vs_EC_p.adj)
%>% unique ),
(all_pST_oneshot_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>%
mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
select(annotation, peptide, fc, p) %>% as.data.frame()
EBC_vs_EC_pST_oneshot_7AA %>% select(peptide, fc, p) %>% write.table(file = "For_motif_analysis/EBC_vs_EC_pST_oneshot.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.1 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.0 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.31 foreach_1.5.2 htmltools_0.5.5
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.39
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.5
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.10 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.6 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.14
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.5-4.1 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.0 lifecycle_1.0.3
## [109] BiocManager_1.30.21 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.0.1
## [121] KernSmooth_2.23-21 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.22 shiny_1.7.4knitr::knit_exit()